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Upgrading the Estimation of Daily PM10 Concentrations Utilizing Prediction Variables Reflecting Atmospheric Processes

Category: Air Pollution Modeling

Volume: 16 | Issue: 9 | Pages: 2245-2254
DOI: 10.4209/aaqr.2016.05.0214
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Konstantinos Dimitriou

  • Laboratory of Meteorology, Department of Physics, University of Ioannina, Ioannina 45110, Greece

Highlights

Combustion emissions are a primary source of PM10 in Birmingham.
Long range transport and wind dispersion variables improved the estimation of PM10.
Extreme intrusions of PM2.5 in Birmingham from continental Europe were indicated.


Abstract

This paper formulates a Multiple Linear Regression Model (MLRM), for the estimation of daily PM10 concentrations in background urban areas. 24-hour backward air mass trajectories, NO2 concentrations and gridded (1° × 1° resolution) Aerosol Optical Depth (AOD) observations from MODIS were used in order to compose the model’s predictor variables. As a supplement to local combustion/non-combustion contributions, the suggested method intends to comprise and quantify the effect that transboundary PM sources and wind dispersion have, on particulate air pollution levels. The proposed technique was implemented at a background sampling site in Birmingham (United Kingdom) and the results were compared with the outcome of a Simple Linear Regression Model (SLRM) which contained only one predictor variable expressing local combustion. Various statistical indices signified the upgraded performance of the MLRM, in comparison with SLRM, thus the participation of long range transport and wind dispersion variables in the MLRM was successful. According to the MLRM’s findings, anthropogenic combustion (traffic, heating) is the strongest source of PM10 in the selected background urban area, followed by local non-combustion emissions and long range transport. Extreme PM2.5 intrusions from continental Europe also emerged.

Keywords

PM10 MODIS Aerosol Optical Depth Wind dispersion Multiple Linear Regression


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